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Nonlinear estimators from ICA mixture models.
- Source :
-
Signal Processing . Feb2019, Vol. 155, p281-286. 6p. - Publication Year :
- 2019
-
Abstract
- Abstract Independent Component Analyzers Mixture Models (ICAMM) are versatile and general models for a large variety of probability density functions. In this paper we assume ICAMM to derive new MAP and LMSE estimators. The first one (MAP-ICAMM) is obtained by an iterative gradient algorithm, while the second (LMSE-ICAMM) admits a closed-form solution. Both estimators can be combined by using LMSE-ICAMM to initialize the iterative computation of MAP-ICAMM.The new estimators are applied to the reconstruction of missed channels in EEG multichannel analysis. The experiments demonstrate the superiority of the new estimators with respect to: Spherical Splines, Hermite, Partial Least Squares, Support Vector Regression, and Random Forest Regression. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01651684
- Volume :
- 155
- Database :
- Academic Search Index
- Journal :
- Signal Processing
- Publication Type :
- Academic Journal
- Accession number :
- 132869149
- Full Text :
- https://doi.org/10.1016/j.sigpro.2018.10.003